Overview

Dataset statistics

Number of variables35
Number of observations5802
Missing cells84707
Missing cells (%)41.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory280.0 B

Variable types

Numeric22
Categorical13

Warnings

mi_death has constant value "0.0" Constant
vital is highly correlated with cvd_deathHigh correlation
prev_mi is highly correlated with prev_mipHigh correlation
prev_mip is highly correlated with prev_miHigh correlation
mi is highly correlated with mip and 1 other fieldsHigh correlation
mip is highly correlated with mi and 4 other fieldsHigh correlation
chd_death is highly correlated with cvd_death and 1 other fieldsHigh correlation
cvd_death is highly correlated with vital and 1 other fieldsHigh correlation
revasc_proc is highly correlated with mip and 3 other fieldsHigh correlation
ptca is highly correlated with mip and 1 other fieldsHigh correlation
cabg is highly correlated with revasc_procHigh correlation
any_chd is highly correlated with mi and 4 other fieldsHigh correlation
any_cvd is highly correlated with mip and 1 other fieldsHigh correlation
prev_revpro is highly correlated with prev_angHigh correlation
prev_ang is highly correlated with prev_revproHigh correlation
mi_date is highly correlated with mip_date and 8 other fieldsHigh correlation
mip_date is highly correlated with mi_date and 6 other fieldsHigh correlation
stk_date is highly correlated with mi_date and 4 other fieldsHigh correlation
chd_dthdt is highly correlated with mi_date and 5 other fieldsHigh correlation
cvd_dthdt is highly correlated with mi_date and 4 other fieldsHigh correlation
ang_date is highly correlated with mi_date and 7 other fieldsHigh correlation
revpro_date is highly correlated with mi_date and 5 other fieldsHigh correlation
ptca_date is highly correlated with mi_date and 3 other fieldsHigh correlation
cabg_date is highly correlated with mi_date and 4 other fieldsHigh correlation
chf_date is highly correlated with mi_date and 6 other fieldsHigh correlation
vital is highly correlated with cvd_deathHigh correlation
prev_mi is highly correlated with prev_mipHigh correlation
prev_mip is highly correlated with prev_mi and 2 other fieldsHigh correlation
mi is highly correlated with mip and 1 other fieldsHigh correlation
mip is highly correlated with mi and 5 other fieldsHigh correlation
chd_death is highly correlated with cvd_death and 1 other fieldsHigh correlation
cvd_death is highly correlated with vital and 1 other fieldsHigh correlation
revasc_proc is highly correlated with mip and 4 other fieldsHigh correlation
ptca is highly correlated with mip and 2 other fieldsHigh correlation
cabg is highly correlated with mip and 1 other fieldsHigh correlation
chf is highly correlated with any_cvdHigh correlation
any_chd is highly correlated with mi and 5 other fieldsHigh correlation
any_cvd is highly correlated with mip and 3 other fieldsHigh correlation
prev_revpro is highly correlated with prev_mip and 1 other fieldsHigh correlation
prev_ang is highly correlated with prev_mip and 1 other fieldsHigh correlation
mi_date is highly correlated with mip_date and 8 other fieldsHigh correlation
mip_date is highly correlated with mi_date and 5 other fieldsHigh correlation
stk_date is highly correlated with mi_date and 2 other fieldsHigh correlation
chd_dthdt is highly correlated with mi_date and 5 other fieldsHigh correlation
cvd_dthdt is highly correlated with mi_date and 4 other fieldsHigh correlation
ang_date is highly correlated with mi_date and 6 other fieldsHigh correlation
revpro_date is highly correlated with mi_date and 4 other fieldsHigh correlation
ptca_date is highly correlated with mi_date and 3 other fieldsHigh correlation
cabg_date is highly correlated with mi_date and 4 other fieldsHigh correlation
chf_date is highly correlated with mi_date and 5 other fieldsHigh correlation
nsrrid is highly correlated with vital and 22 other fieldsHigh correlation
vital is highly correlated with nsrrid and 23 other fieldsHigh correlation
prev_mi is highly correlated with nsrrid and 22 other fieldsHigh correlation
prev_mip is highly correlated with nsrrid and 23 other fieldsHigh correlation
prev_stk is highly correlated with nsrrid and 22 other fieldsHigh correlation
mi is highly correlated with nsrrid and 22 other fieldsHigh correlation
mip is highly correlated with nsrrid and 21 other fieldsHigh correlation
mi_fatal is highly correlated with nsrrid and 23 other fieldsHigh correlation
stroke is highly correlated with nsrrid and 21 other fieldsHigh correlation
stk_fatal is highly correlated with nsrrid and 23 other fieldsHigh correlation
chd_death is highly correlated with nsrrid and 21 other fieldsHigh correlation
cvd_death is highly correlated with nsrrid and 23 other fieldsHigh correlation
angina is highly correlated with vital and 16 other fieldsHigh correlation
revasc_proc is highly correlated with nsrrid and 22 other fieldsHigh correlation
ptca is highly correlated with nsrrid and 23 other fieldsHigh correlation
cabg is highly correlated with nsrrid and 23 other fieldsHigh correlation
chf is highly correlated with nsrrid and 22 other fieldsHigh correlation
prev_chf is highly correlated with nsrrid and 23 other fieldsHigh correlation
any_chd is highly correlated with nsrrid and 22 other fieldsHigh correlation
any_cvd is highly correlated with nsrrid and 17 other fieldsHigh correlation
prev_revpro is highly correlated with nsrrid and 23 other fieldsHigh correlation
mi_death is highly correlated with nsrrid and 23 other fieldsHigh correlation
prev_ang is highly correlated with nsrrid and 23 other fieldsHigh correlation
mi_date is highly correlated with mip_date and 8 other fieldsHigh correlation
mip_date is highly correlated with mi_date and 6 other fieldsHigh correlation
stk_date is highly correlated with cvd_dthdt and 2 other fieldsHigh correlation
chd_dthdt is highly correlated with mi_date and 4 other fieldsHigh correlation
cvd_dthdt is highly correlated with stk_date and 3 other fieldsHigh correlation
ang_date is highly correlated with mi_date and 5 other fieldsHigh correlation
revpro_date is highly correlated with mi_date and 6 other fieldsHigh correlation
ptca_date is highly correlated with mi_date and 5 other fieldsHigh correlation
cabg_date is highly correlated with mi_date and 6 other fieldsHigh correlation
chf_date is highly correlated with mi_date and 3 other fieldsHigh correlation
afibprevalent is highly correlated with nsrrid and 33 other fieldsHigh correlation
afibincident is highly correlated with nsrrid and 33 other fieldsHigh correlation
chd_death is highly correlated with vital and 4 other fieldsHigh correlation
chf_date is highly correlated with mi_date and 7 other fieldsHigh correlation
stk_date is highly correlated with cvd_dthdt and 3 other fieldsHigh correlation
mi_date is highly correlated with chf_date and 7 other fieldsHigh correlation
prev_chf is highly correlated with prev_stkHigh correlation
nsrrid is highly correlated with angina and 1 other fieldsHigh correlation
chd_dthdt is highly correlated with chf_date and 5 other fieldsHigh correlation
revpro_date is highly correlated with chf_date and 6 other fieldsHigh correlation
ptca is highly correlated with any_cvd and 3 other fieldsHigh correlation
cvd_dthdt is highly correlated with chf_date and 5 other fieldsHigh correlation
prev_mip is highly correlated with prev_mi and 1 other fieldsHigh correlation
vital is highly correlated with chd_death and 2 other fieldsHigh correlation
mip_date is highly correlated with chf_date and 6 other fieldsHigh correlation
mi is highly correlated with mip and 1 other fieldsHigh correlation
prev_mi is highly correlated with prev_mip and 1 other fieldsHigh correlation
any_cvd is highly correlated with chd_death and 6 other fieldsHigh correlation
prev_ang is highly correlated with prev_revpro and 1 other fieldsHigh correlation
prev_stk is highly correlated with prev_chfHigh correlation
prev_revpro is highly correlated with prev_ang and 1 other fieldsHigh correlation
chf is highly correlated with prev_mip and 1 other fieldsHigh correlation
ptca_date is highly correlated with chf_date and 8 other fieldsHigh correlation
mip is highly correlated with ptca and 4 other fieldsHigh correlation
cabg is highly correlated with revasc_procHigh correlation
mi_fatal is highly correlated with chd_death and 1 other fieldsHigh correlation
revasc_proc is highly correlated with ptca and 4 other fieldsHigh correlation
cabg_date is highly correlated with chf_date and 6 other fieldsHigh correlation
cvd_death is highly correlated with chd_death and 4 other fieldsHigh correlation
angina is highly correlated with nsrrid and 2 other fieldsHigh correlation
any_chd is highly correlated with chd_death and 6 other fieldsHigh correlation
ang_date is highly correlated with chf_date and 5 other fieldsHigh correlation
afibincident is highly correlated with nsrridHigh correlation
vital is highly correlated with cvd_death and 1 other fieldsHigh correlation
mi is highly correlated with any_chd and 1 other fieldsHigh correlation
cabg is highly correlated with mi_deathHigh correlation
mi_fatal is highly correlated with mi_deathHigh correlation
prev_revpro is highly correlated with mi_deathHigh correlation
chd_death is highly correlated with cvd_death and 2 other fieldsHigh correlation
any_cvd is highly correlated with any_chd and 1 other fieldsHigh correlation
cvd_death is highly correlated with vital and 2 other fieldsHigh correlation
stk_fatal is highly correlated with mi_deathHigh correlation
afibprevalent is highly correlated with mi_death and 1 other fieldsHigh correlation
any_chd is highly correlated with mi and 3 other fieldsHigh correlation
mi_death is highly correlated with vital and 11 other fieldsHigh correlation
afibincident is highly correlated with afibprevalent and 1 other fieldsHigh correlation
prev_mi has 760 (13.1%) missing values Missing
prev_mip has 760 (13.1%) missing values Missing
prev_stk has 760 (13.1%) missing values Missing
mi has 760 (13.1%) missing values Missing
mip has 760 (13.1%) missing values Missing
mi_fatal has 760 (13.1%) missing values Missing
stroke has 760 (13.1%) missing values Missing
stk_fatal has 760 (13.1%) missing values Missing
chd_death has 760 (13.1%) missing values Missing
cvd_death has 760 (13.1%) missing values Missing
angina has 760 (13.1%) missing values Missing
revasc_proc has 760 (13.1%) missing values Missing
ptca has 760 (13.1%) missing values Missing
cabg has 760 (13.1%) missing values Missing
chf has 760 (13.1%) missing values Missing
prev_chf has 760 (13.1%) missing values Missing
any_chd has 760 (13.1%) missing values Missing
any_cvd has 760 (13.1%) missing values Missing
prev_revpro has 2658 (45.8%) missing values Missing
mi_death has 4573 (78.8%) missing values Missing
prev_ang has 3586 (61.8%) missing values Missing
mi_date has 5442 (93.8%) missing values Missing
mip_date has 5447 (93.9%) missing values Missing
stk_date has 5515 (95.1%) missing values Missing
chd_dthdt has 5570 (96.0%) missing values Missing
cvd_dthdt has 5447 (93.9%) missing values Missing
ang_date has 5442 (93.8%) missing values Missing
revpro_date has 5309 (91.5%) missing values Missing
ptca_date has 5491 (94.6%) missing values Missing
cabg_date has 5585 (96.3%) missing values Missing
chf_date has 5184 (89.3%) missing values Missing
afibprevalent has 2866 (49.4%) missing values Missing
afibincident has 2912 (50.2%) missing values Missing
prev_stk is highly skewed (γ1 = 21.26554203) Skewed
nsrrid is uniformly distributed Uniform
nsrrid has unique values Unique
prev_mi has 4701 (81.0%) zeros Zeros
prev_mip has 4641 (80.0%) zeros Zeros
prev_stk has 4889 (84.3%) zeros Zeros
mip has 4327 (74.6%) zeros Zeros
stroke has 4755 (82.0%) zeros Zeros
angina has 2842 (49.0%) zeros Zeros
revasc_proc has 4549 (78.4%) zeros Zeros
ptca has 4731 (81.5%) zeros Zeros
chf has 4424 (76.2%) zeros Zeros
prev_chf has 4902 (84.5%) zeros Zeros
prev_ang has 2004 (34.5%) zeros Zeros

Reproduction

Analysis started2021-09-08 00:35:50.389947
Analysis finished2021-09-08 00:36:50.378245
Duration59.99 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

nsrrid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5802
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202901.8606
Minimum200001
Maximum205804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:50.469117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum200001
5-th percentile200291.05
Q1201451.25
median202901.5
Q3204351.75
95-th percentile205513.95
Maximum205804
Range5803
Interquartile range (IQR)2900.5

Descriptive statistics

Standard deviation1675.54929
Coefficient of variation (CV)0.00825792965
Kurtosis-1.199617301
Mean202901.8606
Median Absolute Deviation (MAD)1450.5
Skewness0.000676325358
Sum1177236595
Variance2807465.425
MonotonicityStrictly increasing
2021-09-07T20:36:50.591464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000011
 
< 0.1%
2038751
 
< 0.1%
2038731
 
< 0.1%
2038721
 
< 0.1%
2038711
 
< 0.1%
2038701
 
< 0.1%
2038691
 
< 0.1%
2038681
 
< 0.1%
2038671
 
< 0.1%
2038661
 
< 0.1%
Other values (5792)5792
99.8%
ValueCountFrequency (%)
2000011
< 0.1%
2000021
< 0.1%
2000031
< 0.1%
2000041
< 0.1%
2000051
< 0.1%
2000061
< 0.1%
2000071
< 0.1%
2000081
< 0.1%
2000091
< 0.1%
2000101
< 0.1%
ValueCountFrequency (%)
2058041
< 0.1%
2058031
< 0.1%
2058021
< 0.1%
2058011
< 0.1%
2058001
< 0.1%
2057991
< 0.1%
2057981
< 0.1%
2057971
< 0.1%
2057961
< 0.1%
2057951
< 0.1%

vital
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size328.8 KiB
1
4497 
0
1305 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5802
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
14497
77.5%
01305
 
22.5%

Length

2021-09-07T20:36:50.809666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:50.873889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
14497
77.5%
01305
 
22.5%

Most occurring characters

ValueCountFrequency (%)
14497
77.5%
01305
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5802
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14497
77.5%
01305
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common5802
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14497
77.5%
01305
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14497
77.5%
01305
 
22.5%

prev_mi
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct17
Distinct (%)0.3%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.1477588259
Minimum0
Maximum22
Zeros4701
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:50.938716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9356311287
Coefficient of variation (CV)6.332150538
Kurtosis182.7625527
Mean0.1477588259
Median Absolute Deviation (MAD)0
Skewness11.95199306
Sum745
Variance0.875405609
MonotonicityNot monotonic
2021-09-07T20:36:51.025553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
04701
81.0%
1256
 
4.4%
218
 
0.3%
317
 
0.3%
514
 
0.2%
89
 
0.2%
66
 
0.1%
44
 
0.1%
103
 
0.1%
73
 
0.1%
Other values (7)11
 
0.2%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04701
81.0%
1256
 
4.4%
218
 
0.3%
317
 
0.3%
44
 
0.1%
514
 
0.2%
66
 
0.1%
73
 
0.1%
89
 
0.2%
92
 
< 0.1%
ValueCountFrequency (%)
221
 
< 0.1%
172
 
< 0.1%
161
 
< 0.1%
152
 
< 0.1%
132
 
< 0.1%
111
 
< 0.1%
103
 
0.1%
92
 
< 0.1%
89
0.2%
73
 
0.1%

prev_mip
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct18
Distinct (%)0.4%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.1743355811
Minimum0
Maximum22
Zeros4641
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:51.119625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9828684327
Coefficient of variation (CV)5.637795947
Kurtosis154.432757
Mean0.1743355811
Median Absolute Deviation (MAD)0
Skewness10.87597188
Sum879
Variance0.9660303559
MonotonicityNot monotonic
2021-09-07T20:36:51.209856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
04641
80.0%
1272
 
4.7%
256
 
1.0%
315
 
0.3%
513
 
0.2%
49
 
0.2%
88
 
0.1%
67
 
0.1%
75
 
0.1%
93
 
0.1%
Other values (8)13
 
0.2%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04641
80.0%
1272
 
4.7%
256
 
1.0%
315
 
0.3%
49
 
0.2%
513
 
0.2%
67
 
0.1%
75
 
0.1%
88
 
0.1%
93
 
0.1%
ValueCountFrequency (%)
221
 
< 0.1%
172
 
< 0.1%
161
 
< 0.1%
152
 
< 0.1%
132
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
103
 
0.1%
93
 
0.1%
88
0.1%

prev_stk
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct12
Distinct (%)0.2%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.05176517255
Minimum0
Maximum18
Zeros4889
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:51.297678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5002457817
Coefficient of variation (CV)9.663751844
Kurtosis590.7088703
Mean0.05176517255
Median Absolute Deviation (MAD)0
Skewness21.26554203
Sum261
Variance0.2502458421
MonotonicityNot monotonic
2021-09-07T20:36:51.396296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
04889
84.3%
1132
 
2.3%
73
 
0.1%
43
 
0.1%
33
 
0.1%
23
 
0.1%
53
 
0.1%
82
 
< 0.1%
61
 
< 0.1%
151
 
< 0.1%
Other values (2)2
 
< 0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04889
84.3%
1132
 
2.3%
23
 
0.1%
33
 
0.1%
43
 
0.1%
53
 
0.1%
61
 
< 0.1%
73
 
0.1%
82
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
181
 
< 0.1%
151
 
< 0.1%
111
 
< 0.1%
82
< 0.1%
73
0.1%
61
 
< 0.1%
53
0.1%
43
0.1%
33
0.1%
23
0.1%

mi
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
4682 
1.0
 
299
2.0
 
47
3.0
 
9
4.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04682
80.7%
1.0299
 
5.2%
2.047
 
0.8%
3.09
 
0.2%
4.05
 
0.1%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:51.603796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:51.674186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04682
92.9%
1.0299
 
5.9%
2.047
 
0.9%
3.09
 
0.2%
4.05
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09724
64.3%
.5042
33.3%
1299
 
2.0%
247
 
0.3%
39
 
0.1%
45
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09724
96.4%
1299
 
3.0%
247
 
0.5%
39
 
0.1%
45
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09724
64.3%
.5042
33.3%
1299
 
2.0%
247
 
0.3%
39
 
0.1%
45
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09724
64.3%
.5042
33.3%
1299
 
2.0%
247
 
0.3%
39
 
0.1%
45
 
< 0.1%

mip
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)0.1%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.2181673939
Minimum0
Maximum6
Zeros4327
Zeros (%)74.6%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:51.756126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6238682857
Coefficient of variation (CV)2.85958536
Kurtosis15.67355463
Mean0.2181673939
Median Absolute Deviation (MAD)0
Skewness3.61692238
Sum1100
Variance0.3892116379
MonotonicityNot monotonic
2021-09-07T20:36:51.840176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04327
74.6%
1447
 
7.7%
2190
 
3.3%
348
 
0.8%
422
 
0.4%
57
 
0.1%
61
 
< 0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04327
74.6%
1447
 
7.7%
2190
 
3.3%
348
 
0.8%
422
 
0.4%
57
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
57
 
0.1%
422
 
0.4%
348
 
0.8%
2190
 
3.3%
1447
 
7.7%
04327
74.6%

mi_fatal
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
4996 
1.0
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04996
86.1%
1.046
 
0.8%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:52.034512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:52.098757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04996
99.1%
1.046
 
0.9%

Most occurring characters

ValueCountFrequency (%)
010038
66.4%
.5042
33.3%
146
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010038
99.5%
146
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010038
66.4%
.5042
33.3%
146
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010038
66.4%
.5042
33.3%
146
 
0.3%

stroke
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct14
Distinct (%)0.3%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.107893693
Minimum0
Maximum17
Zeros4755
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:52.162865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.659014345
Coefficient of variation (CV)6.107996926
Kurtosis193.5207272
Mean0.107893693
Median Absolute Deviation (MAD)0
Skewness11.85057026
Sum544
Variance0.434299907
MonotonicityNot monotonic
2021-09-07T20:36:52.250387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
04755
82.0%
1199
 
3.4%
240
 
0.7%
314
 
0.2%
411
 
0.2%
56
 
0.1%
74
 
0.1%
83
 
0.1%
63
 
0.1%
92
 
< 0.1%
Other values (4)5
 
0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04755
82.0%
1199
 
3.4%
240
 
0.7%
314
 
0.2%
411
 
0.2%
56
 
0.1%
63
 
0.1%
74
 
0.1%
83
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
131
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
92
 
< 0.1%
83
 
0.1%
74
 
0.1%
63
 
0.1%
56
0.1%
411
0.2%

stk_fatal
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
5005 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05005
86.3%
1.037
 
0.6%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:52.442846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:52.504257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.05005
99.3%
1.037
 
0.7%

Most occurring characters

ValueCountFrequency (%)
010047
66.4%
.5042
33.3%
137
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010047
99.6%
137
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010047
66.4%
.5042
33.3%
137
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010047
66.4%
.5042
33.3%
137
 
0.2%

chd_death
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
4807 
1.0
 
235

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04807
82.9%
1.0235
 
4.1%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:52.657450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:52.717954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04807
95.3%
1.0235
 
4.7%

Most occurring characters

ValueCountFrequency (%)
09849
65.1%
.5042
33.3%
1235
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09849
97.7%
1235
 
2.3%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09849
65.1%
.5042
33.3%
1235
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09849
65.1%
.5042
33.3%
1235
 
1.6%

cvd_death
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
4683 
1.0
 
359

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04683
80.7%
1.0359
 
6.2%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:52.867032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:52.927591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04683
92.9%
1.0359
 
7.1%

Most occurring characters

ValueCountFrequency (%)
09725
64.3%
.5042
33.3%
1359
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09725
96.4%
1359
 
3.6%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09725
64.3%
.5042
33.3%
1359
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09725
64.3%
.5042
33.3%
1359
 
2.4%

angina
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct8
Distinct (%)0.2%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.4821499405
Minimum0
Maximum7
Zeros2842
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:52.984395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6431075207
Coefficient of variation (CV)1.33383304
Kurtosis13.75795494
Mean0.4821499405
Median Absolute Deviation (MAD)0
Skewness2.365310132
Sum2431
Variance0.4135872832
MonotonicityNot monotonic
2021-09-07T20:36:53.065564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
02842
49.0%
12072
35.7%
271
 
1.2%
333
 
0.6%
411
 
0.2%
56
 
0.1%
65
 
0.1%
72
 
< 0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
02842
49.0%
12072
35.7%
271
 
1.2%
333
 
0.6%
411
 
0.2%
56
 
0.1%
65
 
0.1%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
65
 
0.1%
56
 
0.1%
411
 
0.2%
333
 
0.6%
271
 
1.2%
12072
35.7%
02842
49.0%

revasc_proc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)0.1%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.1241570805
Minimum0
Maximum5
Zeros4549
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:53.149843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4268041784
Coefficient of variation (CV)3.437614485
Kurtosis26.61760681
Mean0.1241570805
Median Absolute Deviation (MAD)0
Skewness4.528987552
Sum626
Variance0.1821618067
MonotonicityNot monotonic
2021-09-07T20:36:53.232825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
04549
78.4%
1401
 
6.9%
260
 
1.0%
325
 
0.4%
45
 
0.1%
52
 
< 0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04549
78.4%
1401
 
6.9%
260
 
1.0%
325
 
0.4%
45
 
0.1%
52
 
< 0.1%
ValueCountFrequency (%)
52
 
< 0.1%
45
 
0.1%
325
 
0.4%
260
 
1.0%
1401
 
6.9%
04549
78.4%

ptca
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)0.1%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.08052360175
Minimum0
Maximum5
Zeros4731
Zeros (%)81.5%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:53.310598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3521992619
Coefficient of variation (CV)4.373863741
Kurtosis38.93747878
Mean0.08052360175
Median Absolute Deviation (MAD)0
Skewness5.588629925
Sum406
Variance0.1240443201
MonotonicityNot monotonic
2021-09-07T20:36:53.394232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
04731
81.5%
1242
 
4.2%
247
 
0.8%
319
 
0.3%
42
 
< 0.1%
51
 
< 0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04731
81.5%
1242
 
4.2%
247
 
0.8%
319
 
0.3%
42
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
42
 
< 0.1%
319
 
0.3%
247
 
0.8%
1242
 
4.2%
04731
81.5%

cabg
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
4825 
1.0
 
215
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04825
83.2%
1.0215
 
3.7%
2.02
 
< 0.1%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:53.584264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:53.648141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04825
95.7%
1.0215
 
4.3%
2.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09867
65.2%
.5042
33.3%
1215
 
1.4%
22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09867
97.8%
1215
 
2.1%
22
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09867
65.2%
.5042
33.3%
1215
 
1.4%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09867
65.2%
.5042
33.3%
1215
 
1.4%
22
 
< 0.1%

chf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)0.3%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.2707259024
Minimum0
Maximum13
Zeros4424
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:53.712696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9692207386
Coefficient of variation (CV)3.580081292
Kurtosis38.82846414
Mean0.2707259024
Median Absolute Deviation (MAD)0
Skewness5.459797586
Sum1365
Variance0.9393888402
MonotonicityNot monotonic
2021-09-07T20:36:53.806853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
04424
76.2%
1321
 
5.5%
2122
 
2.1%
361
 
1.1%
451
 
0.9%
524
 
0.4%
616
 
0.3%
78
 
0.1%
96
 
0.1%
85
 
0.1%
Other values (3)4
 
0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04424
76.2%
1321
 
5.5%
2122
 
2.1%
361
 
1.1%
451
 
0.9%
524
 
0.4%
616
 
0.3%
78
 
0.1%
85
 
0.1%
96
 
0.1%
ValueCountFrequency (%)
131
 
< 0.1%
122
 
< 0.1%
101
 
< 0.1%
96
 
0.1%
85
 
0.1%
78
 
0.1%
616
 
0.3%
524
 
0.4%
451
0.9%
361
1.1%

prev_chf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)0.1%
Missing760
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.03827846093
Minimum0
Maximum7
Zeros4902
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:53.894845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2808481278
Coefficient of variation (CV)7.336975443
Kurtosis220.2350166
Mean0.03827846093
Median Absolute Deviation (MAD)0
Skewness12.44403301
Sum193
Variance0.07887567091
MonotonicityNot monotonic
2021-09-07T20:36:53.974845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04902
84.5%
1113
 
1.9%
215
 
0.3%
35
 
0.1%
44
 
0.1%
72
 
< 0.1%
51
 
< 0.1%
(Missing)760
 
13.1%
ValueCountFrequency (%)
04902
84.5%
1113
 
1.9%
215
 
0.3%
35
 
0.1%
44
 
0.1%
51
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
51
 
< 0.1%
44
 
0.1%
35
 
0.1%
215
 
0.3%
1113
 
1.9%
04902
84.5%

any_chd
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
4249 
1.0
793 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04249
73.2%
1.0793
 
13.7%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:54.167045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:54.226979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04249
84.3%
1.0793
 
15.7%

Most occurring characters

ValueCountFrequency (%)
09291
61.4%
.5042
33.3%
1793
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09291
92.1%
1793
 
7.9%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09291
61.4%
.5042
33.3%
1793
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09291
61.4%
.5042
33.3%
1793
 
5.2%

any_cvd
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing760
Missing (%)13.1%
Memory size325.2 KiB
0.0
3846 
1.0
1196 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15126
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03846
66.3%
1.01196
 
20.6%
(Missing)760
 
13.1%

Length

2021-09-07T20:36:54.380013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:54.439951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03846
76.3%
1.01196
 
23.7%

Most occurring characters

ValueCountFrequency (%)
08888
58.8%
.5042
33.3%
11196
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10084
66.7%
Other Punctuation5042
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08888
88.1%
11196
 
11.9%
Other Punctuation
ValueCountFrequency (%)
.5042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08888
58.8%
.5042
33.3%
11196
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08888
58.8%
.5042
33.3%
11196
 
7.9%

prev_revpro
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.2%
Missing2658
Missing (%)45.8%
Memory size288.2 KiB
0.0
3002 
1.0
 
134
2.0
 
6
3.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9432
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03002
51.7%
1.0134
 
2.3%
2.06
 
0.1%
3.01
 
< 0.1%
4.01
 
< 0.1%
(Missing)2658
45.8%

Length

2021-09-07T20:36:54.617636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:54.682531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03002
95.5%
1.0134
 
4.3%
2.06
 
0.2%
3.01
 
< 0.1%
4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
06146
65.2%
.3144
33.3%
1134
 
1.4%
26
 
0.1%
31
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6288
66.7%
Other Punctuation3144
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06146
97.7%
1134
 
2.1%
26
 
0.1%
31
 
< 0.1%
41
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.3144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06146
65.2%
.3144
33.3%
1134
 
1.4%
26
 
0.1%
31
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06146
65.2%
.3144
33.3%
1134
 
1.4%
26
 
0.1%
31
 
< 0.1%
41
 
< 0.1%

mi_death
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing4573
Missing (%)78.8%
Memory size250.8 KiB
0.0
1229 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3687
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01229
 
21.2%
(Missing)4573
78.8%

Length

2021-09-07T20:36:54.860057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:54.920250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01229
100.0%

Most occurring characters

ValueCountFrequency (%)
02458
66.7%
.1229
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2458
66.7%
Other Punctuation1229
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02458
100.0%
Other Punctuation
ValueCountFrequency (%)
.1229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02458
66.7%
.1229
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02458
66.7%
.1229
33.3%

prev_ang
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct8
Distinct (%)0.4%
Missing3586
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean0.1371841155
Minimum0
Maximum8
Zeros2004
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:54.972527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5179818577
Coefficient of variation (CV)3.775815121
Kurtosis57.29780859
Mean0.1371841155
Median Absolute Deviation (MAD)0
Skewness6.202741653
Sum304
Variance0.2683052049
MonotonicityNot monotonic
2021-09-07T20:36:55.063044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
02004
34.5%
1158
 
2.7%
233
 
0.6%
314
 
0.2%
53
 
0.1%
42
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
(Missing)3586
61.8%
ValueCountFrequency (%)
02004
34.5%
1158
 
2.7%
233
 
0.6%
314
 
0.2%
42
 
< 0.1%
53
 
0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
71
 
< 0.1%
53
 
0.1%
42
 
< 0.1%
314
 
0.2%
233
 
0.6%
1158
 
2.7%
02004
34.5%

mi_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct345
Distinct (%)95.8%
Missing5442
Missing (%)93.8%
Infinite0
Infinite (%)0.0%
Mean2066.708333
Minimum19
Maximum4747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:55.176853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile312.05
Q11103.5
median1985
Q33000.5
95-th percentile4126.15
Maximum4747
Range4728
Interquartile range (IQR)1897

Descriptive statistics

Standard deviation1197.181305
Coefficient of variation (CV)0.5792695976
Kurtosis-0.96217422
Mean2066.708333
Median Absolute Deviation (MAD)904
Skewness0.1803956975
Sum744015
Variance1433243.076
MonotonicityNot monotonic
2021-09-07T20:36:55.300187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26872
 
< 0.1%
33822
 
< 0.1%
34242
 
< 0.1%
5672
 
< 0.1%
18572
 
< 0.1%
11902
 
< 0.1%
30652
 
< 0.1%
24532
 
< 0.1%
29982
 
< 0.1%
23512
 
< 0.1%
Other values (335)340
 
5.9%
(Missing)5442
93.8%
ValueCountFrequency (%)
191
< 0.1%
211
< 0.1%
311
< 0.1%
621
< 0.1%
831
< 0.1%
901
< 0.1%
1191
< 0.1%
1201
< 0.1%
1441
< 0.1%
1541
< 0.1%
ValueCountFrequency (%)
47471
< 0.1%
46151
< 0.1%
44731
< 0.1%
44221
< 0.1%
43681
< 0.1%
43661
< 0.1%
43221
< 0.1%
43201
< 0.1%
43161
< 0.1%
42771
< 0.1%

mip_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct337
Distinct (%)94.9%
Missing5447
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean2049.214085
Minimum19
Maximum5208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:55.422020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile183.2
Q1963.5
median1970
Q32993.5
95-th percentile4143.3
Maximum5208
Range5189
Interquartile range (IQR)2030

Descriptive statistics

Standard deviation1253.70312
Coefficient of variation (CV)0.6117970443
Kurtosis-0.981290789
Mean2049.214085
Median Absolute Deviation (MAD)1013
Skewness0.1987595451
Sum727471
Variance1571771.513
MonotonicityNot monotonic
2021-09-07T20:36:55.551132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17952
 
< 0.1%
16072
 
< 0.1%
8382
 
< 0.1%
12822
 
< 0.1%
38002
 
< 0.1%
15552
 
< 0.1%
17672
 
< 0.1%
38892
 
< 0.1%
12672
 
< 0.1%
13942
 
< 0.1%
Other values (327)335
 
5.8%
(Missing)5447
93.9%
ValueCountFrequency (%)
191
< 0.1%
211
< 0.1%
361
< 0.1%
411
< 0.1%
431
< 0.1%
441
< 0.1%
641
< 0.1%
901
< 0.1%
1111
< 0.1%
1121
< 0.1%
ValueCountFrequency (%)
52081
< 0.1%
50061
< 0.1%
46681
< 0.1%
46151
< 0.1%
45781
< 0.1%
45571
< 0.1%
45201
< 0.1%
45081
< 0.1%
43201
< 0.1%
42621
< 0.1%

stk_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct282
Distinct (%)98.3%
Missing5515
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean1770.132404
Minimum4
Maximum5154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:55.675657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile143.5
Q1670.5
median1657
Q32600
95-th percentile3879.8
Maximum5154
Range5150
Interquartile range (IQR)1929.5

Descriptive statistics

Standard deviation1205.190434
Coefficient of variation (CV)0.6808476198
Kurtosis-0.7635456918
Mean1770.132404
Median Absolute Deviation (MAD)978
Skewness0.4189553672
Sum508028
Variance1452483.982
MonotonicityNot monotonic
2021-09-07T20:36:55.804375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
382
 
< 0.1%
33342
 
< 0.1%
15652
 
< 0.1%
30652
 
< 0.1%
12662
 
< 0.1%
16081
 
< 0.1%
10461
 
< 0.1%
12921
 
< 0.1%
39561
 
< 0.1%
21151
 
< 0.1%
Other values (272)272
 
4.7%
(Missing)5515
95.1%
ValueCountFrequency (%)
41
< 0.1%
181
< 0.1%
382
< 0.1%
391
< 0.1%
541
< 0.1%
721
< 0.1%
741
< 0.1%
901
< 0.1%
941
< 0.1%
981
< 0.1%
ValueCountFrequency (%)
51541
< 0.1%
45721
< 0.1%
45401
< 0.1%
45391
< 0.1%
44641
< 0.1%
42121
< 0.1%
40981
< 0.1%
40721
< 0.1%
40651
< 0.1%
40131
< 0.1%

chd_dthdt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct228
Distinct (%)98.3%
Missing5570
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean2441.099138
Minimum78
Maximum4491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:55.931203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile484.05
Q11525
median2607
Q33340.25
95-th percentile4102.45
Maximum4491
Range4413
Interquartile range (IQR)1815.25

Descriptive statistics

Standard deviation1147.993983
Coefficient of variation (CV)0.4702774931
Kurtosis-0.9314860733
Mean2441.099138
Median Absolute Deviation (MAD)901
Skewness-0.2816551793
Sum566335
Variance1317890.185
MonotonicityNot monotonic
2021-09-07T20:36:56.048551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29102
 
< 0.1%
43662
 
< 0.1%
14972
 
< 0.1%
33352
 
< 0.1%
24861
 
< 0.1%
16871
 
< 0.1%
19481
 
< 0.1%
13911
 
< 0.1%
5271
 
< 0.1%
15881
 
< 0.1%
Other values (218)218
 
3.8%
(Missing)5570
96.0%
ValueCountFrequency (%)
781
< 0.1%
851
< 0.1%
1271
< 0.1%
1381
< 0.1%
1621
< 0.1%
2561
< 0.1%
2891
< 0.1%
3481
< 0.1%
3791
< 0.1%
3921
< 0.1%
ValueCountFrequency (%)
44911
< 0.1%
44101
< 0.1%
43801
< 0.1%
43662
< 0.1%
42601
< 0.1%
42511
< 0.1%
42361
< 0.1%
42221
< 0.1%
41771
< 0.1%
41581
< 0.1%

cvd_dthdt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct338
Distinct (%)95.2%
Missing5447
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean2495.678873
Minimum7
Maximum4758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:56.166784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile445.8
Q11546
median2622
Q33486.5
95-th percentile4240.5
Maximum4758
Range4751
Interquartile range (IQR)1940.5

Descriptive statistics

Standard deviation1200.61071
Coefficient of variation (CV)0.4810757999
Kurtosis-0.9770704665
Mean2495.678873
Median Absolute Deviation (MAD)954
Skewness-0.2434691946
Sum885966
Variance1441466.077
MonotonicityNot monotonic
2021-09-07T20:36:56.292405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15072
 
< 0.1%
41022
 
< 0.1%
36492
 
< 0.1%
10442
 
< 0.1%
43662
 
< 0.1%
13272
 
< 0.1%
14972
 
< 0.1%
31502
 
< 0.1%
33352
 
< 0.1%
5992
 
< 0.1%
Other values (328)335
 
5.8%
(Missing)5447
93.9%
ValueCountFrequency (%)
71
< 0.1%
781
< 0.1%
851
< 0.1%
991
< 0.1%
1271
< 0.1%
1351
< 0.1%
1381
< 0.1%
1421
< 0.1%
1621
< 0.1%
2561
< 0.1%
ValueCountFrequency (%)
47581
< 0.1%
47521
< 0.1%
46061
< 0.1%
45001
< 0.1%
44921
< 0.1%
44911
< 0.1%
44761
< 0.1%
44101
< 0.1%
44011
< 0.1%
43801
< 0.1%

ang_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct350
Distinct (%)97.2%
Missing5442
Missing (%)93.8%
Infinite0
Infinite (%)0.0%
Mean1642.011111
Minimum21
Maximum4760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:56.413394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile151.35
Q1666.25
median1390.5
Q32452.25
95-th percentile3944.15
Maximum4760
Range4739
Interquartile range (IQR)1786

Descriptive statistics

Standard deviation1191.529833
Coefficient of variation (CV)0.7256527223
Kurtosis-0.6098877187
Mean1642.011111
Median Absolute Deviation (MAD)877.5
Skewness0.6293718335
Sum591124
Variance1419743.342
MonotonicityNot monotonic
2021-09-07T20:36:56.536050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2662
 
< 0.1%
11902
 
< 0.1%
3902
 
< 0.1%
18572
 
< 0.1%
3052
 
< 0.1%
8282
 
< 0.1%
5392
 
< 0.1%
21902
 
< 0.1%
20122
 
< 0.1%
4192
 
< 0.1%
Other values (340)340
 
5.9%
(Missing)5442
93.8%
ValueCountFrequency (%)
211
< 0.1%
311
< 0.1%
361
< 0.1%
421
< 0.1%
501
< 0.1%
521
< 0.1%
541
< 0.1%
581
< 0.1%
621
< 0.1%
661
< 0.1%
ValueCountFrequency (%)
47601
< 0.1%
46801
< 0.1%
45641
< 0.1%
43681
< 0.1%
43411
< 0.1%
43161
< 0.1%
42661
< 0.1%
42121
< 0.1%
41481
< 0.1%
41091
< 0.1%

revpro_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct463
Distinct (%)93.9%
Missing5309
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean1953.841785
Minimum21
Maximum5231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:56.659363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile245.8
Q1910
median1789
Q32855
95-th percentile4143.4
Maximum5231
Range5210
Interquartile range (IQR)1945

Descriptive statistics

Standard deviation1238.336607
Coefficient of variation (CV)0.6337957436
Kurtosis-0.817048814
Mean1953.841785
Median Absolute Deviation (MAD)961
Skewness0.3880846214
Sum963244
Variance1533477.552
MonotonicityNot monotonic
2021-09-07T20:36:56.788062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9783
 
0.1%
38893
 
0.1%
15553
 
0.1%
4042
 
< 0.1%
19252
 
< 0.1%
37012
 
< 0.1%
21902
 
< 0.1%
14082
 
< 0.1%
24822
 
< 0.1%
362
 
< 0.1%
Other values (453)470
 
8.1%
(Missing)5309
91.5%
ValueCountFrequency (%)
211
< 0.1%
362
< 0.1%
411
< 0.1%
431
< 0.1%
441
< 0.1%
641
< 0.1%
961
< 0.1%
1081
< 0.1%
1111
< 0.1%
1121
< 0.1%
ValueCountFrequency (%)
52311
< 0.1%
52081
< 0.1%
50061
< 0.1%
47481
< 0.1%
46681
< 0.1%
46441
< 0.1%
46131
< 0.1%
45781
< 0.1%
45571
< 0.1%
45201
< 0.1%

ptca_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct295
Distinct (%)94.9%
Missing5491
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean2038.289389
Minimum21
Maximum5231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:56.909068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile240
Q1935.5
median1915
Q33010.5
95-th percentile4210.5
Maximum5231
Range5210
Interquartile range (IQR)2075

Descriptive statistics

Standard deviation1256.72429
Coefficient of variation (CV)0.6165583244
Kurtosis-0.9303607187
Mean2038.289389
Median Absolute Deviation (MAD)1037
Skewness0.2631229462
Sum633908
Variance1579355.942
MonotonicityNot monotonic
2021-09-07T20:36:57.037259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14082
 
< 0.1%
37982
 
< 0.1%
8372
 
< 0.1%
17402
 
< 0.1%
15542
 
< 0.1%
14532
 
< 0.1%
20502
 
< 0.1%
7632
 
< 0.1%
29982
 
< 0.1%
14192
 
< 0.1%
Other values (285)291
 
5.0%
(Missing)5491
94.6%
ValueCountFrequency (%)
211
< 0.1%
331
< 0.1%
361
< 0.1%
371
< 0.1%
961
< 0.1%
1081
< 0.1%
1111
< 0.1%
1281
< 0.1%
1291
< 0.1%
1341
< 0.1%
ValueCountFrequency (%)
52311
< 0.1%
50061
< 0.1%
47481
< 0.1%
46681
< 0.1%
46441
< 0.1%
45771
< 0.1%
45191
< 0.1%
45071
< 0.1%
44391
< 0.1%
43801
< 0.1%

cabg_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct209
Distinct (%)96.3%
Missing5585
Missing (%)96.3%
Infinite0
Infinite (%)0.0%
Mean1914.073733
Minimum36
Maximum5208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:57.159194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile272
Q1881
median1792
Q32702
95-th percentile4098.4
Maximum5208
Range5172
Interquartile range (IQR)1821

Descriptive statistics

Standard deviation1224.567147
Coefficient of variation (CV)0.639770102
Kurtosis-0.7816124681
Mean1914.073733
Median Absolute Deviation (MAD)911
Skewness0.4320163219
Sum415354
Variance1499564.698
MonotonicityNot monotonic
2021-09-07T20:36:57.286239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11902
 
< 0.1%
4062
 
< 0.1%
21902
 
< 0.1%
3652
 
< 0.1%
33342
 
< 0.1%
26122
 
< 0.1%
11132
 
< 0.1%
2242
 
< 0.1%
31781
 
< 0.1%
17861
 
< 0.1%
Other values (199)199
 
3.4%
(Missing)5585
96.3%
ValueCountFrequency (%)
361
< 0.1%
371
< 0.1%
641
< 0.1%
1051
< 0.1%
1521
< 0.1%
1601
< 0.1%
1791
< 0.1%
2242
< 0.1%
2321
< 0.1%
2681
< 0.1%
ValueCountFrequency (%)
52081
< 0.1%
46691
< 0.1%
46131
< 0.1%
44331
< 0.1%
44281
< 0.1%
42801
< 0.1%
41521
< 0.1%
41501
< 0.1%
41441
< 0.1%
41321
< 0.1%

chf_date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct571
Distinct (%)92.4%
Missing5184
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean2020.166667
Minimum9
Maximum4608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2021-09-07T20:36:57.406254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile164
Q1948
median1929
Q33065.5
95-th percentile4092.4
Maximum4608
Range4599
Interquartile range (IQR)2117.5

Descriptive statistics

Standard deviation1242.407817
Coefficient of variation (CV)0.6150026318
Kurtosis-1.095747762
Mean2020.166667
Median Absolute Deviation (MAD)1038.5
Skewness0.1673167865
Sum1248463
Variance1543577.183
MonotonicityNot monotonic
2021-09-07T20:36:57.525476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23743
 
0.1%
1643
 
0.1%
11413
 
0.1%
28563
 
0.1%
24533
 
0.1%
6862
 
< 0.1%
41482
 
< 0.1%
6962
 
< 0.1%
31962
 
< 0.1%
33982
 
< 0.1%
Other values (561)593
 
10.2%
(Missing)5184
89.3%
ValueCountFrequency (%)
91
< 0.1%
151
< 0.1%
192
< 0.1%
221
< 0.1%
231
< 0.1%
321
< 0.1%
341
< 0.1%
501
< 0.1%
551
< 0.1%
601
< 0.1%
ValueCountFrequency (%)
46081
< 0.1%
45801
< 0.1%
44991
< 0.1%
44841
< 0.1%
44541
< 0.1%
44381
< 0.1%
44021
< 0.1%
43701
< 0.1%
43681
< 0.1%
43631
< 0.1%

afibprevalent
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing2866
Missing (%)49.4%
Memory size284.1 KiB
0.0
2890 
1.0
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8808
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02890
49.8%
1.046
 
0.8%
(Missing)2866
49.4%

Length

2021-09-07T20:36:57.730085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:57.792976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02890
98.4%
1.046
 
1.6%

Most occurring characters

ValueCountFrequency (%)
05826
66.1%
.2936
33.3%
146
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5872
66.7%
Other Punctuation2936
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05826
99.2%
146
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.2936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05826
66.1%
.2936
33.3%
146
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05826
66.1%
.2936
33.3%
146
 
0.5%

afibincident
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing2912
Missing (%)50.2%
Memory size283.2 KiB
0.0
2558 
1.0
332 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8670
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02558
44.1%
1.0332
 
5.7%
(Missing)2912
50.2%

Length

2021-09-07T20:36:57.948145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-07T20:36:58.009710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02558
88.5%
1.0332
 
11.5%

Most occurring characters

ValueCountFrequency (%)
05448
62.8%
.2890
33.3%
1332
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5780
66.7%
Other Punctuation2890
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05448
94.3%
1332
 
5.7%
Other Punctuation
ValueCountFrequency (%)
.2890
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8670
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05448
62.8%
.2890
33.3%
1332
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII8670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05448
62.8%
.2890
33.3%
1332
 
3.8%

Interactions

2021-09-07T20:35:56.263904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:56.375865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:56.479666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:56.582763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:56.705579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:56.812194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:56.925693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.036147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.142741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.248463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.356681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.469748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.587032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.689640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.788337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.889696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:57.983686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:59.536501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:59.648084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:59.751836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:59.850270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:35:59.947597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.058055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.159964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.254484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.347028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.450878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.549447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.652228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.753143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.851774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:00.949404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.048267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.154226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.260923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.357742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.452407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.540471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.623264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.711160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.797094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:01.904196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.005938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.103983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.200101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.302015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.394124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.492059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.594223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.692563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.796330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.899915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:02.998916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.093955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.192689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.293798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.396816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.485153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.579081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.666642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.750976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.838467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:03.926753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.032467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.133660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.232841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.328774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.441960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.545392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.651811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.765818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.874926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:04.990091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.104439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.211129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.318582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.429129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.542096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.659189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.761048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.855774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:05.958218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.053411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.153619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.252375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.357263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.458789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.551635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.669986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.777914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.878991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:06.982405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:39.606775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:39.829424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:40.026270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:40.243458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.330752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.431530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.528888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.621252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.718082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.800758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.898640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:40.991176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:41.081088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:41.162957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:41.262882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:41.362347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:41.461621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:41.561327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:41.664867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:42.623878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:42.814851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:42.910672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:43.004422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:43.176671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:43.353382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:43.448711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:43.820021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:43.915043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.013568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.106615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.199326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.299490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.386045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.466792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.554592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:44.645015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:45.076399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:45.165885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:45.246691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:45.350974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:45.445545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-07T20:36:45.642634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:45.739505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:45.838897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:45.939332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.047308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.152647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.250739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.355038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.453426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.535245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.614385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.700857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.780206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.860540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:46.944977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:47.027749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:47.109038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-07T20:36:47.189094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-07T20:36:58.829682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-07T20:36:59.176902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-07T20:36:59.472775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-07T20:36:47.461360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-07T20:36:48.505167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-07T20:36:49.067076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-07T20:36:50.199271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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